Current rapid synthesis approaches for reusable dedicated hardware components (cores) for digital signal processing systems are ineffective since they fail to capture and exploit the manner in which the resulting components are used as part of a heterogeneous system. This leads to counter-productive core redesign for each use of the core. This paper presents a solution to this issue which combines a novel but intuitive system modeling technique and associated core generation and integration methodology which generates reuable core architectures which may be optimised via algorithm level transformations. For an example design problem, these provide an effective rapid core synthesis and implementation exploration flow which allows a factor 3.9 throughput increase with no extra hardware expense.

Using graphical and statistical methods, geochemical data from 21 allegedly contaminated ground water wells in northeastern Pennsylvania were compared with data from historical ground water, Marcellus flow-back fluid, and other contaminated waters. The graphical methods included box and whisker plots, Piper diagrams, Stiff diagrams, and Cl/Br vs. Cl cross-plots. The statistical methods included summary statistics, analysis of variance, and discriminant analysis. The geochemical data collected for this study included the following major ions, trace metals, nutrients, and physical properties: sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), barium (Ba), strontium (Sr), manganese (Mn), iron (Fe), aluminum (Al), chloride (Cl), bromide (Br), sulfate (SO 4), arsenic (As), nitrate-nitrite as N, total Kjeldahl nitrogen, total nitrogen, alkalinity, and total dissolved solids (TDS). The graphical and statistical results show that none of the 2012-2013 ground water wells were detectably impacted by flow-back fluids. Instead, the results show that at least one well is contaminated with animal waste or septic effluent. Discriminant analysis of the 2012-2013 ground water samples supports this observation. The remaining wells are geochemically similar to historical ground water wells both graphically and statistically. These findings suggest that the major and trace element geochemistry of northeastern Pennsylvania ground water has not been detectably influenced by flow-back fluid spills at these 21 sites.

No abstract

No abstract

Carnegie Mellon's Wearable Computers Laboratory has built four generations of real-time speech translation wearable computers, culminating in the Speech Translator Smart Module. Smart Modules are a family of interoperable modules supporting real-time speech recognition, language translation, and speech synthesis. In this paper, we examine the effect of various design factors on performance with emphasis on modularity and scalability. A system-level approach to power / performance optimization is described that improved the metric of (performance / (weight * volume * power)) by over a factor of 300 through the four generations.